Background: The data-driven inference of intracellular networks is one of the key challenges of computational and systems biology. As suggested by recent works, a simple yet effective approach for reconstructing regulatory networks comprises the following two steps. First, the observed effects induced by directed perturbations are collected in a signed and directed perturbation graph (PG). In a second step, Transitive Reduction (TR) is used to identify and eliminate those edges in the PG that can be explained by paths and are therefore likely to reflect indirect effects. Results: In this work we introduce novel variants for PG generation and TR, leading to significantly improved performances. The key modifications concern: (i) use of novel statistical criteria for deriving a high-quality PG from experimental data; (ii) the application of local TR which allows only short paths to explain (and remove) a given edge; and (iii) a novel strategy to rank the edges with respect to their confidence. To compare the new methods with existing ones we not only apply them to a recent DREAM network inference challenge but also to a novel and unprecedented synthetic compendium consisting of 30 5000-gene networks simulated with varying biological and measurement error variances resulting in a total of 270 datasets. The benchmarks clearly demonstrate the superior reconstruction performance of the novel PG and TR variants compared to existing approaches. Moreover, the benchmark enabled us to draw some general conclusions. For example, it turns out that local TR restricted to paths with a length of only two is often sufficient or even favorable. We also demonstrate that considering edge weights is highly beneficial for TR whereas consideration of edge signs is of minor importance. We explain these observations from a graph-theoretical perspective and discuss the consequences with respect to a greatly reduced computational demand to conduct TR. Finally, as a realistic application scenario, we use our framework for inferring gene interactions in yeast based on a library of gene expression data measured in mutants with single knockouts of transcription factors. The reconstructed network shows a significant enrichment of known interactions, especially within the 100 most confident (and for experimental validation most relevant) edges. Conclusions: This paper presents several major achievements. The novel methods introduced herein can be seen as state of the art for inference techniques relying on perturbation graphs and transitive reduction. Another key result of the study is the generation of a new and unprecedented large-scale in silico benchmark dataset accounting for different noise levels and providing a solid basis for unbiased testing of network inference methodologies. Finally, applying our approach to Saccharomyces cerevisiae suggested several new gene interactions with high confidence awaiting experimental validation.

Rooftop photovoltaic solar systems can be an essential tool to support the energy transition of Europe. The assessment of solar power generation potential in urban areas, necessary for smart grid planning, requires the processing of data of different types, such as building cadastral information, a detailed description of available roof areas, and solar irradiation data. We introduce an algorithm for the fast calculation of the building’s shadows and a procedure for the integration of solar irradiation in time. We therefore develop a methodology that allows a fast evaluation with minimal computational resources, and we apply it to an urban scenario of a medium-sized European city obtaining an estimate of the complete census PV power generation potential, with a spatial resolution of 1 m. We validate the results by comparison with a reference procedure, obtaining minimal deviation with a much lower demand for computational resources.

A paradigm shift is apparent in Chip Multiprocessor (CMP) design, as the new performance bottleneck is becoming communication rather than computation. It is widely provisioned that number of cores on a single chip will reach thousands in a decade. Thus, new high rate interconnects such as optical or RF have been proposed by various researchers. However, these interconnect structures fail to provide essential requirements of heterogeneous on-chip traffic; bandwidth reconfigurability and broadcast support with a low complex design. In this paper we investigate the feasibility of a new Orthogonal Frequency Division Multiple Access (OFDMA) RF interconnect for the first time to the best of our knowledge. In addition we provide a novel dynamic bandwidth arbitration and modulation order selection policy, that is designed regarding the bimodal on-chip packets. The proposed approach decreases the average latency up to 3.5 times compared to conventional static approach.

The implementation of the energy transition and the building of energy communities are driving forward the exploitation of the potential for rooftop photovoltaic power generation. Estimating rooftop PV generation potential requires the processing of different types of data, such as the cadastral information of buildings, a detailed description of available rooftop areas, and solar irradiance data. High-resolution estimation based on GIS data is normally limited to small survey areas. Instead, by using an algorithm for the efficient calculation of shadows over rooftops, and the integration of solar irradiance over time, we developed a procedure that allows for the rapid full census assessment of rooftop photovoltaic potential with a spatial resolution of 1 m, applicable at the regional scale and requiring minimal computational resources. We applied this approach to the rooftops of buildings in Sardinia, an island and region of Italy of particular interest for the energy transition. In addition to estimating the geographic potential, we carried out a preliminary assessment of the technical and economic potential, yielding a maximal photovoltaic rooftop generation potential of 22 TWh for the entire region.

Recent advances in vision system integration allows to design new integrated sensors which are able to achieve complexes tasks. The 'Cyclope' project aims to develop a novel integrated sensor for real-time 3D reconstruction. It includes an active vision sensor, a digital centralized processing architecture and a communication block. This sensor is designed to obtain a monolithic integrate vision system. This paper presents the digital processing architecture which is a massively-parallel architecture in a totally scalable IP to assure the compatibility with application constraints. This architecture has been implemented on an FPGA circuit and we demonstrate, with accurate results, the realtime operations.

The prediction and monitoring of plant diseases and pests are key activities in agriculture. These activities enable growers to take preventive measures to reduce the spread of diseases and harmful insects. Consequently, they reduce crop loss, make pesticide and resource use more efficient, and preserve plant health, contributing to environmental sustainability. We illustrate the SMARTerra decision support system, which processes daily measured and predicted weather data, spatially interpolating them at high resolution across the entire Sardinia region. From these data, SMARTerra generates risk predictions for plant pests and diseases. Currently, models for predicting the risk of rice blast disease and the hatching of locust eggs are implemented in the infrastructure. The web interface of the SMARTerra platform allows users to visualize detailed risk maps and promptly take preventive measures. A simple notification system is also implemented to directly alert emergency responders. Model outputs by the SMARTerra infrastructure are comparable with results from in-field observations produced by the LAORE Regional Agency. The infrastructure provides a database for storing the time series and risk maps generated, which can be used by agencies and researchers to conduct further analysis.